Add dataset and wit.

dataset/MNBVC.py

@@ -0,0 +1,5 @@
+from datasets import load_dataset
+
+dataset = load_dataset("liwu/MNBVC", "wikipedia", split="train", streaming=True)
+
+print(next(iter(dataset)))  # get the first line

wit/__init__.py

@@ -0,0 +1,2 @@
+from qwen.modeling_qwen import QWenLMHeadModel
+from qwen.configuration_qwen import QWenConfig

wit/configuration_qwen.py

@@ -0,0 +1,45 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+class QWenConfig:
+    def __init__(self):
+        self.vocab_size = 151936
+        self.hidden_size = 2048
+        self.num_hidden_layers = 24
+        self.num_attention_heads = 16
+        self.emb_dropout_prob = 0.0
+        self.attn_dropout_prob = 0.0
+        self.layer_norm_epsilon = 1e-6
+        self.initializer_range = 0.02
+        self.max_position_embeddings = 8192
+        self.scale_attn_weights = True
+        self.use_cache = True
+        self.bf16 = False
+        self.fp16 = False
+        self.fp32 = False
+        self.kv_channels = 128
+        self.rotary_pct = 1.0
+        self.rotary_emb_base = 10000
+        self.use_dynamic_ntk = True
+        self.use_logn_attn = True
+        self.use_flash_attn = "auto"
+        self.intermediate_size = 11008
+        self.no_bias = True
+        self.tie_word_embeddings = False
+        self.use_cache_quantization = False
+        self.use_cache_kernel = False
+        self.softmax_in_fp32 = False
+
+        self.chat_format = "chatml"
+        self.eos_token_id = 151643
+        self.pad_token_id = 151643
+        self.max_window_size = 6144
+        self.max_new_tokens = 512
+        self.do_sample = True
+        self.top_k = 0
+        self.top_p = 0.8
+        self.repetition_penalty = 1.1
+        self.model_max_length = 8192

wit/demo.py

@@ -0,0 +1,31 @@
+import torch
+from modelscope import snapshot_download
+
+from modeling_wit import QWenLMHeadModel
+from modeling_wit import QwenRunner
+from configuration_qwen import QWenConfig
+from tokenization_qwen import QWenTokenizer
+
+seed = 4321
+torch.manual_seed(seed)
+torch.cuda.manual_seed_all(seed)
+
+model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
+# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
+
+config = QWenConfig()
+model = QWenLMHeadModel(config)
+
+print(model)
+
+tokenizer = QWenTokenizer("./qwen.tiktoken")
+model = model.from_pretrained(model_dir).cuda()
+
+model = model.eval()
+# model = model.train()  # control by @torch.no_grad()
+
+
+runner = QwenRunner(model)
+
+response, history, decode_tokens = runner.Chat(tokenizer, "东南亚国家日本的首都是什么市", "")
+print(decode_tokens)

wit/model.safetensors.index.json

@@ -0,0 +1,202 @@
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+}

wit/modeling_wit.py

@@ -0,0 +1,389 @@
+import copy
+import math
+import os
+import sys
+import gc
+from tqdm import auto as tqdm_lib
+import json
+from typing import Optional, Tuple, Union, Callable, List, Any, Generator
+from einops import rearrange
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.nn import CrossEntropyLoss
+from torch import nn
+from safetensors.torch import load_file as safe_load_file
+from safetensors.torch import save_file as safe_save_file
+
+from qwen_generation_utils import (
+    make_context,
+    decode_tokens,
+)
+
+sys.path.append("..")
+from tools import show
+from tools import mem_tracker
+
+# tracker = mem_tracker.MemTracker()
+# tracker.track()
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self._norm(x.float()).type_as(x) * self.weight
+
+
+class QWenAttention(nn.Module):
+    def __init__(self, config, index):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.split_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.projection_size = config.kv_channels * config.num_attention_heads
+        self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)
+        self.c_proj = nn.Linear(config.hidden_size, self.projection_size, bias=not config.no_bias)
+        self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
+        self.index = index
+
+    def _split_heads(self, tensor, num_heads, attn_head_size):
+        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
+        tensor = tensor.view(new_shape)
+        return tensor
+
+    def _merge_heads(self, tensor, num_heads, attn_head_size):
+        tensor = tensor.contiguous()
+        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
+        return tensor.view(new_shape)
+
+
+class QWenMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        ff_dim_in = config.intermediate_size // 2
+        self.w1 = nn.Linear(config.hidden_size, ff_dim_in, bias=not config.no_bias)
+        self.w2 = nn.Linear(config.hidden_size, ff_dim_in, bias=not config.no_bias)
+        self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)
+
+
+class QWenBlock(nn.Module):
+    def __init__(self, config, index):
+        super().__init__()
+        self.ln_1 = RMSNorm(
+            config.hidden_size,
+            eps=config.layer_norm_epsilon,
+        )
+        self.attn = QWenAttention(config, index)
+        self.ln_2 = RMSNorm(
+            config.hidden_size,
+            eps=config.layer_norm_epsilon,
+        )
+        self.mlp = QWenMLP(config)
+        self.index = index
+
+
+class QWenModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.drop = nn.Dropout(config.emb_dropout_prob)
+        dim = config.kv_channels
+
+        self.h = nn.ModuleList([QWenBlock(config, i) for i in range(config.num_hidden_layers)])
+        self.ln_f = RMSNorm(
+            config.hidden_size,
+            eps=config.layer_norm_epsilon,
+        )
+
+        self.dim = dim
+        self.base = config.rotary_emb_base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._rotary_pos_emb_cache = None
+        self._seq_len_cached = 0
+        self._ntk_alpha_cached = 1.0
+
+    def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0):
+        if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
+            base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
+            self.inv_freq = 1.0 / (
+                base ** (torch.arange(0, self.dim, 2, device=self.inv_freq.device).float() / self.dim)
+            )
+            self._seq_len_cached = max(2 * seqlen, 16)
+            self._ntk_alpha_cached = ntk_alpha
+            seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
+            freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
+
+            emb = torch.cat((freqs, freqs), dim=-1)
+            emb = rearrange(emb, "n d -> 1 n 1 d")
+
+            cos, sin = emb.cos(), emb.sin()
+            self._rotary_pos_emb_cache = [cos, sin]
+
+
+class QWenLMHeadModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.transformer = QWenModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]]):
+        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
+        resolved_archive_file = os.path.join(pretrained_model_name_or_path, "model.safetensors.index.json")
+        print(f"loading weights file {resolved_archive_file}")
+        with open(resolved_archive_file, "r") as f:
+            index = json.loads(f.read())
+        shard_filenames = sorted(set(index["weight_map"].values()))
+        resolved_archive_file = [os.path.join(pretrained_model_name_or_path, f) for f in shard_filenames]
+        model = cls._load_pretrained_model(resolved_archive_file)
+        return model
+
+    def _load_state_dict_into_model(self, model_to_load, state_dict, start_prefix):
+        metadata = getattr(state_dict, "_metadata", None)
+        state_dict = state_dict.copy()
+        if metadata is not None:
+            state_dict._metadata = metadata
+        error_msgs = []
+
+        def load(module: nn.Module, state_dict, prefix=""):
+            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
+            args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
+            if len([key for key in state_dict if key.startswith(prefix)]) > 0:
+                module._load_from_state_dict(*args)
+
+            for name, child in module._modules.items():
+                if child is not None:
+                    load(child, state_dict, prefix + name + ".")
+
+        load(model_to_load, state_dict, prefix=start_prefix)
+        del state_dict
+        return error_msgs
+
+    def _load_pretrained_model(cls, resolved_archive_file):
+        start_prefix = ""
+        model_to_load = cls
+        if len(resolved_archive_file) > 1:
+            resolved_archive_file = tqdm_lib.tqdm(resolved_archive_file, desc="Loading checkpoint shards")
+        for shard_file in resolved_archive_file:
+            state_dict = safe_load_file(shard_file)
+            cls._load_state_dict_into_model(model_to_load, state_dict, start_prefix)
+            del state_dict  # force memory release
+            gc.collect()
+        print(f"All model checkpoint weights were used when initializing {cls.__class__.__name__}.\n")
+        return cls
+
+
+class QwenRunner:
+    def __init__(self, qwen):
+        self.qwen = qwen
+
+    @torch.no_grad()
+    def Chat(
+        self,
+        tokenizer,
+        query: str,
+        query_assistant: str,
+        system: str = "You are a helpful assistant.",
+        history=[],
+    ):
+        qwen = self.qwen
+        history = copy.deepcopy(history)
+        raw_text, context_tokens = self.prepareInput(tokenizer, query, query_assistant, history, system)
+        input_ids = torch.tensor([context_tokens]).to(next(qwen.parameters()).device)
+        self.unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
+        while True:
+            outputs = self.forwardQWen(input_ids)
+            next_token_scores = outputs[:, -1, :]
+
+            next_token_scores = self.repetition_penalty(input_ids, next_token_scores)
+            next_token_scores = self.top_p(next_token_scores)
+            next_tokens = self.sample(next_token_scores)
+            finish, next_tokens = self.isFinish(next_tokens)
+            if finish:
+                break
+            input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+
+        decoded, response, end_reason = decode_tokens(
+            input_ids[0],
+            tokenizer,
+            raw_text_len=len(raw_text),
+            context_length=len(context_tokens),
+            errors="replace",
+        )
+        history.append((query, response))
+        return input_ids[0].cpu().tolist(), history, decoded
+
+    def _rotate_half(self, x):
+        x = rearrange(x, "... (j d) -> ... j d", j=2)
+        x1, x2 = x.unbind(dim=-2)
+        return torch.cat((-x2, x1), dim=-1)
+
+    def apply_rotary_pos_emb(self, t, freqs):
+        rot_dim = freqs[0].shape[-1]
+        cos, sin = freqs
+        t_float = t.float()
+        t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:]
+        t_rot = (t_rot * cos) + (self._rotate_half(t_rot) * sin)
+        return torch.cat((t_rot, t_pass), dim=-1).type_as(t)
+
+    def split_heads(
+        self,
+        attention,
+        hidden_states: Optional[Tuple[torch.FloatTensor]],
+    ):
+        atten = attention
+        mixed_x_layer = atten.c_attn(hidden_states)
+        query, key, value = mixed_x_layer.split(atten.split_size, dim=2)
+        query = atten._split_heads(query, atten.num_heads, atten.head_dim)
+        key = atten._split_heads(key, atten.num_heads, atten.head_dim)
+        value = atten._split_heads(value, atten.num_heads, atten.head_dim)
+        return query, key, value
+
+    def pos_emb(self, query, key, rotary_pos_emb_list):
+        rotary_pos_emb = rotary_pos_emb_list[0]
+        rotary_pos_emb = [i[:, -query.shape[1] :, :, :] for i in rotary_pos_emb]
+        rotary_pos_emb = (rotary_pos_emb,) * 2
+        query = self.apply_rotary_pos_emb(query, rotary_pos_emb[0])
+        key = self.apply_rotary_pos_emb(key, rotary_pos_emb[1])
+        return query, key
+
+    def attention(self, attention, query, key, value, causal_mask):
+        query = query.permute(0, 2, 1, 3)
+        key = key.permute(0, 2, 1, 3)
+        value = value.permute(0, 2, 1, 3)
+        attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=causal_mask).transpose(1, 2)
+        context_layer = attention._merge_heads(attn_output, attention.num_heads, attention.head_dim)
+        attn_output = attention.c_proj(context_layer)
+        return attn_output
+
+    def build_mask(self, query):
+        size = query.size(1)
+        causal_mask = torch.tril(torch.ones((size, size), dtype=torch.bool, device=query.device)).view(1, 1, size, size)
+        return causal_mask
+
+    def forwardAttention(
+        self,
+        attention,
+        hidden_states: Optional[Tuple[torch.FloatTensor]],
+        rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
+    ):
+        query, key, value = self.split_heads(attention, hidden_states)
+        query, key = self.pos_emb(query, key, rotary_pos_emb_list)
+        causal_mask = self.build_mask(query)
+        return self.attention(attention, query, key, value, causal_mask)
+
+    def forwardQWenBlock(
+        self,
+        block,
+        hidden_states: Optional[Tuple[torch.FloatTensor]],
+        rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
+    ):
+        layernorm_output = block.ln_1(hidden_states)
+
+        attn_outputs = self.forwardAttention(block.attn, layernorm_output, rotary_pos_emb_list)
+        attn_output = attn_outputs[0]
+        layernorm_input = attn_output + hidden_states
+
+        layernorm_output = block.ln_2(layernorm_input)
+        a1 = block.mlp.w1(layernorm_output)
+        a2 = block.mlp.w2(layernorm_output)
+        intermediate_parallel = a1 * F.silu(a2)
+        mlp_output = block.mlp.c_proj(intermediate_parallel)
+
+        hidden_states = layernorm_input + mlp_output
+        return hidden_states
+
+    def forwardQWen(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+    ):
+        transfm = self.qwen.transformer
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+        hidden_states = transfm.wte(input_ids)
+        kv_seq_len = hidden_states.size()[1]
+
+        transfm.update_rotary_pos_emb_cache(kv_seq_len, ntk_alpha=1.0)
+        cos, sin = transfm._rotary_pos_emb_cache
+        rotary_pos_emb_list = [[cos[:, :kv_seq_len], sin[:, :kv_seq_len]]]
+
+        hidden_states = transfm.drop(hidden_states)
+        output_shape = input_shape + (hidden_states.size(-1),)
+
+        for block in transfm.h:
+            hidden_states = self.forwardQWenBlock(block, hidden_states, rotary_pos_emb_list=rotary_pos_emb_list)
+
+        hidden_states = transfm.ln_f(hidden_states)
+        hidden_states = hidden_states.view(output_shape)
+
+        lm_logits = self.qwen.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(lm_logits.device)
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+        # shift_labels = torch.ones([1,19]).to(lm_logits.device).to(torch.int64)
+        # shift_logits = lm_logits[..., :-1, :].contiguous()
+        # loss_fct = CrossEntropyLoss()
+        # loss = loss_fct(
+        #     shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
+        # )
+        # loss.backward()
+
+        return lm_logits
+
+    def prepareInput(self, tokenizer, query, query_assistant, history, system):
+        return make_context(tokenizer, query, query_assistant, history=history, system=system)
+
+    def repetition_penalty(self, input_ids, next_token_scores):
+        penalty = self.qwen.config.repetition_penalty
+        score = torch.gather(next_token_scores, 1, input_ids)
+        # if score < 0 then repetition penalty has to be multiplied to reduce the token probabilities
+        score = torch.where(score < 0, score * penalty, score / penalty)
+        next_token_scores = next_token_scores.scatter_(1, input_ids, score)
+        return next_token_scores
+
+    def top_p(self, next_token_scores):
+        top_p = self.qwen.config.top_p
+        filter_value = -float("Inf")
+        min_tokens_to_keep = 1
+        sorted_logits, sorted_indices = torch.sort(next_token_scores, descending=False)
+        cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
+        # Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
+        sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
+        # Keep at least min_tokens_to_keep
+        sorted_indices_to_remove[..., -min_tokens_to_keep:] = 0
+        # scatter sorted tensors to original indexing
+        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+        next_token_scores = next_token_scores.masked_fill(indices_to_remove, filter_value)
+        return next_token_scores
+
+    def sample(self, next_token_scores):
+        probs = nn.functional.softmax(next_token_scores, dim=-1)
+        next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+        return next_tokens
+
+    def isFinish(self, next_tokens):
+        pad_token_id = self.qwen.config.pad_token_id
+        eos_token_id_tensor = torch.tensor([self.qwen.config.eos_token_id]).to(next_tokens.device)
+
+        next_tokens = next_tokens * self.unfinished_sequences + pad_token_id * (1 - self.unfinished_sequences)
+        self.unfinished_sequences = self.unfinished_sequences.mul(
+            next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
+        )
+        return self.unfinished_sequences.max() == 0, next_tokens[:, None]

wit/qwen_generation_utils.py

@@ -0,0 +1,109 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Generation support."""
+
+from typing import Tuple, List, Union, Iterable
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from transformers import PreTrainedTokenizer
+from transformers import logging
+from transformers.generation import LogitsProcessor
+
+logger = logging.get_logger(__name__)
+
+# Types.
+HistoryType = List[Tuple[str, str]]
+TokensType = List[int]
+BatchTokensType = List[List[int]]
+
+
+def make_context(
+    tokenizer: PreTrainedTokenizer,
+    query: str,
+    query_assistant: str = "",
+    history: List[Tuple[str, str]] = None,
+    system: str = "",
+    max_window_size: int = 6144,
+):
+    if history is None:
+        history = []
+
+    im_start, im_end = "<|im_start|>", "<|im_end|>"
+    im_start_tokens = [tokenizer.im_start_id]
+    im_end_tokens = [tokenizer.im_end_id]
+    nl_tokens = tokenizer.encode("\n")
+
+    def _tokenize_str(role, content):
+        return f"{role}\n{content}", tokenizer.encode(role, allowed_special=set()) + nl_tokens + tokenizer.encode(
+            content, allowed_special=set()
+        )
+
+    system_text, system_tokens_part = _tokenize_str("system", system)
+    system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
+    assistant_tokens = tokenizer.encode(query_assistant, allowed_special=set())
+    raw_text = ""
+    context_tokens = []
+
+    for turn_query, turn_response in reversed(history):
+        query_text, query_tokens_part = _tokenize_str("user", turn_query)
+        query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
+        response_text, response_tokens_part = _tokenize_str("assistant", turn_response)
+        response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
+
+        next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
+        prev_chat = f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
+
+        current_context_size = len(system_tokens) + len(next_context_tokens) + len(context_tokens)
+        if current_context_size < max_window_size:
+            context_tokens = next_context_tokens + context_tokens
+            raw_text = prev_chat + raw_text
+        else:
+            break
+
+    context_tokens = system_tokens + context_tokens
+    raw_text = f"{im_start}{system_text}{im_end}" + raw_text
+    context_tokens += (
+        nl_tokens
+        + im_start_tokens
+        + _tokenize_str("user", query)[1]
+        + im_end_tokens
+        + nl_tokens
+        + im_start_tokens
+        + tokenizer.encode("assistant")
+        + nl_tokens
+        + assistant_tokens
+    )
+    raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n{query_assistant}"
+
+    return raw_text, context_tokens
+
+
+def decode_tokens(
+    tokens: Union[torch.LongTensor, TokensType],
+    tokenizer: PreTrainedTokenizer,
+    raw_text_len: int = 0,
+    context_length: int = 0,
+    errors: str = "replace",
+) -> str:
+    if torch.is_tensor(tokens):
+        tokens = tokens.cpu().numpy().tolist()
+
+    end_reason = f"Gen length {len(tokens)}"
+    eod_token_idx = context_length
+    for eod_token_idx in range(context_length, len(tokens)):
+        if tokens[eod_token_idx] in [tokenizer.im_start_id, tokenizer.im_end_id]:
+            end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
+            break
+
+    decoded = tokenizer.decode(tokens, errors=errors)
+
+    decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)
+    trim_decode_tokens = decode_tokens[raw_text_len:]
+    trim_decode_tokens = trim_decode_tokens.strip()
+
+    return decoded, trim_decode_tokens, end_reason

wit/tokenization_qwen.py

@@ -0,0 +1,266 @@
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Tokenization classes for QWen."""
+
+import base64
+import logging
+import os
+import unicodedata
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+import tiktoken
+from transformers import PreTrainedTokenizer, AddedToken
+
+logger = logging.getLogger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
+
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
+ENDOFTEXT = "<|endoftext|>"
+IMSTART = "<|im_start|>"
+IMEND = "<|im_end|>"
+# as the default behavior is changed to allow special tokens in
+# regular texts, the surface forms of special tokens need to be
+# as different as possible to minimize the impact
+EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
+# changed to use actual index to avoid misconfiguration with vocabulary expansion
+SPECIAL_START_ID = 151643
+SPECIAL_TOKENS = tuple(
+    enumerate(
+        (
+            (
+                ENDOFTEXT,
+                IMSTART,
+                IMEND,
+            )
+            + EXTRAS
+        ),
+        start=SPECIAL_START_ID,
+    )
+)
+SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+    with open(tiktoken_bpe_file, "rb") as f:
+        contents = f.read()
+    return {
+        base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line)
+    }
+
+
+class QWenTokenizer(PreTrainedTokenizer):
+    """QWen tokenizer."""
+
+    vocab_files_names = VOCAB_FILES_NAMES
+
+    def __init__(
+        self,
+        vocab_file,
+        errors="replace",
+        extra_vocab_file=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # how to handle errors in decoding UTF-8 byte sequences
+        # use ignore if you are in streaming inference
+        self.errors = errors
+
+        self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)  # type: Dict[bytes, int]
+        self.special_tokens = {token: index for index, token in SPECIAL_TOKENS}
+
+        # try load extra vocab from file
+        if extra_vocab_file is not None:
+            used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
+            extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
+            for token, index in extra_mergeable_ranks.items():
+                if token in self.mergeable_ranks:
+                    logger.info(f"extra token {token} exists, skipping")
+                    continue
+                if index in used_ids:
+                    logger.info(f"the index {index} for extra token {token} exists, skipping")
+                    continue
+                self.mergeable_ranks[token] = index
+            # the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
+
+        enc = tiktoken.Encoding(
+            "Qwen",
+            pat_str=PAT_STR,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        assert (
+            len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
+        ), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
+
+        self.decoder = {v: k for k, v in self.mergeable_ranks.items()}  # type: dict[int, bytes|str]
+        self.decoder.update({v: k for k, v in self.special_tokens.items()})
+
+        self.tokenizer = enc  # type: tiktoken.Encoding
+
+        self.eod_id = self.tokenizer.eot_token
+        self.im_start_id = self.special_tokens[IMSTART]
+        self.im_end_id = self.special_tokens[IMEND]
+
+    def __getstate__(self):
+        # for pickle lovers
+        state = self.__dict__.copy()
+        del state["tokenizer"]
+        return state
+
+    def __setstate__(self, state):
+        # tokenizer is not python native; don't pass it; rebuild it
+        self.__dict__.update(state)
+        enc = tiktoken.Encoding(
+            "Qwen",
+            pat_str=PAT_STR,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        self.tokenizer = enc
+
+    def __len__(self) -> int:
+        return self.tokenizer.n_vocab
+
+    def get_vocab(self) -> Dict[bytes, int]:
+        return self.mergeable_ranks
+
+    def convert_tokens_to_ids(self, tokens: Union[bytes, str, List[Union[bytes, str]]]) -> List[int]:
+        ids = []
+        if isinstance(tokens, (str, bytes)):
+            if tokens in self.special_tokens:
+                return self.special_tokens[tokens]
+            else:
+                return self.mergeable_ranks.get(tokens)
+        for token in tokens:
+            if token in self.special_tokens:
+                ids.append(self.special_tokens[token])
+            else:
+                ids.append(self.mergeable_ranks.get(token))
+        return ids
+
+    def _add_tokens(
+        self,
+        new_tokens: Union[List[str], List[AddedToken]],
+        special_tokens: bool = False,
+    ) -> int:
+        if not special_tokens and new_tokens:
+            raise ValueError("Adding regular tokens is not supported")
+        for token in new_tokens:
+            surface_form = token.content if isinstance(token, AddedToken) else token
+            if surface_form not in SPECIAL_TOKENS_SET:
+                raise ValueError("Adding unknown special tokens is not supported")
+        return 0
+
+    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+        """
+        Save only the vocabulary of the tokenizer (vocabulary).
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        file_path = os.path.join(save_directory, "qwen.tiktoken")
+        with open(file_path, "w", encoding="utf8") as w:
+            for k, v in self.mergeable_ranks.items():
+                line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+                w.write(line)
+        return (file_path,)
+
+    def tokenize(
+        self,
+        text: str,
+        allowed_special: Union[Set, str] = "all",
+        disallowed_special: Union[Collection, str] = (),
+        **kwargs,
+    ) -> List[Union[bytes, str]]:
+        """
+        Converts a string in a sequence of tokens.
+
+        Args:
+            text (`str`):
+                The sequence to be encoded.
+            allowed_special (`Literal["all"]` or `set`):
+                The surface forms of the tokens to be encoded as special tokens in regular texts.
+                Default to "all".
+            disallowed_special (`Literal["all"]` or `Collection`):
+                The surface forms of the tokens that should not be in regular texts and trigger errors.
+                Default to an empty tuple.
+
+            kwargs (additional keyword arguments, *optional*):
+                Will be passed to the underlying model specific encode method.
+
+        Returns:
+            `List[bytes|str]`: The list of tokens.
+        """
+        tokens = []
+        text = unicodedata.normalize("NFC", text)
+
+        # this implementation takes a detour: text -> token id -> token surface forms
+        for t in self.tokenizer.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special):
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors=self.errors)
+                    temp = b""
+                text += t
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type types or str")
+        if temp:
+            text += temp.decode("utf-8", errors=self.errors)
+        return text
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_vocab
+
+    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+        """Converts an id to a token, special tokens included"""
+        if index in self.decoder:
+            return self.decoder[index]
+        raise ValueError("unknown ids")
+
+    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+        """Converts a token to an id using the vocab, special tokens included"""
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        if token in self.mergeable_ranks:
+            return self.mergeable_ranks[token]
+        raise ValueError("unknown token")
+
+    def _tokenize(self, text: str, **kwargs):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+
+        Do NOT take care of added tokens.
+        """
+        raise NotImplementedError
+
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        errors: str = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        if skip_special_tokens:
+            token_ids = [i for i in token_ids if i < self.eod_id]
+        return self.tokenizer.decode(token_ids, errors=errors or self.errors)

wit/tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+  "model_max_length": 8192,
+  "tokenizer_class": "QWenTokenizer",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_qwen.QWenTokenizer",
+      null
+      ]
+  }
+}